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    Adaptive Neural Control for Safe Human-Robot Interaction

    Rahimi Nohooji H 2017.pdf (1.645Mb)
    Access Status
    Open access
    Authors
    Rahimi Nohooji, Hamed
    Date
    2017
    Supervisor
    Prof. Ian Howard
    Type
    Thesis
    Award
    PhD
    
    Metadata
    Show full item record
    Faculty
    Science and Engineering
    School
    Mechanical Engineering
    URI
    http://hdl.handle.net/20.500.11937/68285
    Collection
    • Curtin Theses
    Abstract

    This thesis studies safe human-robot interaction utilizing the neural adaptive control design. First, novel tangent and secant barrier Lyapunov functions are constructed to provide stable position and velocity constrained controls, respectively. Then, neural backpropagation and the concept of the inverse differential Riccati equation are utilized to achieve the impedance adaption control for assistive human-robot interaction, and the optimal robot-environment interaction control, respectively. Finally, adaptive neural assist-as-needed control is developed for assistive robotic rehabilitation.

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